Overview

The aim was to prepare a spatio-temporal representation of valuation studies related to biodiversity and ecosystem services and … .

1 Creation of the Corpus

2 Georeferencing the Corpus

To identify country names in the corpus of literature a two step approach was used. First, we wanted to understand where studies were conducted and searched the title, abstract, and keywords of each paper for country names. Second, to understand where the funding institutions were located we searched the affiliations, acknowledgments, and funding text for country names.

The input data we used are the following:

Bib file downloaded from Web of Science
ISO 3166-1 alpha-3 country codes (https://www.iso.org/iso-3166-country-codes.html)
IPBES regional and subregional area dataset (https://doi.org/10.5281/zenodo.3923633)

The python code used to georeference the corpus can be found here. An overview of the pipeline is provided in the following schematic and described below.

knitr::include_graphics("pilot2.svg")

Overview of the process of Georeferencing the corpus of valuation studies

Step 1: Extract country names from text Country names were extracted from the title, abstract, and keywords of each paper with a regular expression and the associated ISO code was added into a a column in the dataset. The same regular expression was also used to search the affiliations, acknowledgments, and funding text of the same paper and placed into a second column.

Step 2 and 3: Bundle countries in regions The IPBES Regions and Subregions datatset was then used to add additional region and subregion attributes to the dataset by matching the ISO3 code.

Step 4: Find TS accordingly Finally, we used a set of files to add additional attributes to the dataset that identified the topics. The set of files contained identifying information for papers derived from sets of web of science searches targeting particular topics. This identifying information was then matched to the corpus, and the topic extracted.

Finally, the complete corpus with the added attributes of country ISO codes of both funding institutions and research locations, and topic identification were used as the basis of the rest of the research project.

3 Valuation Maps

3.1 Geographic Distribution

3.1.1 Density of studies

3.1.2 Density of institutions

3.1.3 Summarized by region and subregion

Density of studies by region

Density of studies by subregion

Density of institutions by region

Density of institutions by subregion

3.2 Relationships between density of studies and institutions

knitr::include_graphics("Outputs/Corpus/Names1_Names2.png")

Density of studies vs density of institutions

3.3 Temporal Trends

4 Indicator Analysis

The IPBES Core Indicators were used alongside a chosen set of other relevant indicators to understand geographic trends between the density of studies and density of institutes.

4.1 Selection of Indicators

4.1.1 IPBES Core Indicators

We used all the most recent year of the IPBES Core Indicators available within the country dataset except for two indicators, Countries/Regions with Active NBSAP and Category 1 nations in CTIES, as these are binary in the dataset and would not be compatible with the following analysis. We selected a specific category from the indicators with multiple categories. For example, for the indicator “Area of forest production under FSC and PEFC certification” we chose the FSC certification area and not the PEFC certification area.

Here is the table of all of the IPBES Core Indicators used, the category selected, the year the data is from, and the number assigned to them.

Table 1: Details of the IPBES Core Indicators Used
Name	Category	Year	Number
Area of forest production under FSC and PEFC certification	FSC_area	2016	1
Biodiversity Habitat Index	Average	2014	2
Biodiversity Intactness Index	Value	2005	3
Biocapacity per capita	Value - Total	2012	4
Ecological Footprint per capita	Value - Total	2012	5
Forest area	Forest area (1000ha)	2015	6
Water Footprint	Water Footprint - Total (Mm3/y)	2013	7
Inland Fishery Production	Capture	2015	8
Region-based Marine Trophic Index	1950	2014	9
Nitrogen + Phosphate Fertilizers	N total nutrients - Consumption in nutrients	2014	10
Nitrogen Use Efficiency (%)	Nitrogen Use Efficiency (%)	2009	11
Percentage and total area covered by protected areas	Terrestrial - Protected Area (%)	2017	13
Percentage of undernourished people	Prevalence of undernourishment (%) (3-year average)	2015	15
Proportion of local breeds, classified as being at risk, not-at-risk or unknown level of risk of extinction	At Risk of Extinction	2016	16
PA of Key Biodiversity Areas Coverage (%)	Estimate	2016	17
Protected area management effectiveness	PA Assessed on Management Effectiveness (%)	2015	18
Protected Area Connectedness Index	Protected Area Connectedness Index	2012	19
Species Habitat Index	Species Habitat Index	2014	20
Species Protection Index (%)	Species Protection Index (%)	2014	21
Species Status Information Index	Value	2014	22
Total Wood Removals (roundwood, m3)	Total	2014	23
Trends in forest extent (tree cover)	Percentage of Tree Cover Loss	2015	24
Nitrogen Deposition Trends (kg N/ha/yr)	Nitrogen Deposition Trends (kg N/ha/yr)	2030	25
Trends in Pesticides Use	Use of pesticides (3-year average)	2013	26

There were a few instances of duplicated values which were double checked with the original dataset and the erranous value removed. Examples include having two values for USA due to the separation of Hawaii in the original dataset. In these cases Hawaii was removed and the value referring to the rest of the states of the country was used instead. Additionally, Indicator 9, Region-based Marine Trophic Index, the mean of the regions was calculated per country, as countries such as Germany have multiple regions with distinct values.

4.1.2 Other Indicators

A set of other indicators were included in the analysis to expand the coverage of socioeconomic variables. We included the human development index (HDI), average harmonized learning outcomes score, gross domestic product (GDP), corruption perception index (CPI), and population.

These datasets were downloaded, cleaned, and had ISO3 codes added to easily merge them into the analysis. The latest data available was used for each indicator.

4.2 Dataset Compilation

To understand how valuation is spread across geographies, we counted the number of times each country’s ISO code appeared in the corpus for both geography columns added in step 2. The result is the density of studies per country and the density of funding institutions per country for the entire corpus.

The external indicators were also joined onto the dataset to analyze the relationships between these socioeconomic indicators and the density of studies and funding institutions.

This process was also repeated with an additional filter that excluded any studies published before 2010.
## Pearson Correlation Analysis

To summarize this analysis more concisely, we ran a pearson correlation to understand the relationship between the number of studies filtered by _____ and each of the indicators. The results are shown below.

knitr::include_graphics("Outputs/Pearson_correlation_table/correlation_figure.png")

Pearson correlations of geographic valuation studies and indicators

knitr::include_graphics("Outputs/Pearson_correlation_table/correlation_figure_log.png")

Pearson correlations of log geographic valuation studies and the indicators

4.3 Individual Trend Analysis

The trend between each indicator and the density of studies and funding institutions are shown here for the entire corpus.

Area of forest production under FSC and PEFC certification

Biodiversity Habitat Index

Biodiversity Intactness Index

Biocapacity per capita

Ecological Footprint per capita

Forest area

Water Footprint

Inland Fishery Production

Region-based Marine Trophic Index

Nitrogen + Phosphate Fertilizers

Nitrogen Use Efficiency (%)

Percentage and total area covered by protected areas

Percentage of undernourished people

Proportion of local breeds, classified as being at risk, not-at-risk or unknown level of risk of extinction

PA of Key Biodiversity Areas Coverage (%)

Protected area management effectiveness

Protected Area Connectedness Index

Species Habitat Index

Species Protection Index (%)

Species Status Information Index

Total Wood Removals (roundwood, m3)

Trends in forest extent (tree cover)

Nitrogen Deposition Trends (kg N/ha/yr)

Trends in Pesticides Use

Human Development Index

knitr::include_graphics("Outputs/Corpus/Names1_HDI.png")

HDI vs. Density of Studies and Institutions

knitr::include_graphics("Outputs/Corpus/Names2_HDI.png")